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Usage: |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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question_template = "# Question\n\n{question}\n\n# Solution\n\n" |
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model_name = "ScalableMath/llemma-7b-sft-prm800k-level-1to3-hf" |
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto") |
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tokenizer = AutoTokenizer.from_pretrained("EleutherAI/llemma_7b") |
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question = "Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $ |
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(r,\\theta),$ where $r > 0$ and $0 \\le \\theta < 2 \\pi.$" |
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question = question_template.format(question=question) |
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input_tensor = torch.tensor([tokenizer.encode(question)]) |
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outputs = model.generate(input_tensor.to(model.device), max_new_tokens=500) |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(result) |
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``` |
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Example Results: |
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``` |
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# Question |
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Convert the point $(0,3)$ in rectangular coordinates to polar coordinates. Enter your answer in the form $(r,\theta),$ where $r > 0$ and $0 \le \theta < 2 \pi.$ |
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# Solution |
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To convert from rectangular to polar coordinates, I need to use the formulas $r = \sqrt{x^2 + y^2}$ and $\theta = \tan^{-1}(y/x).$ |
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In this case, $x = 0$ and $y = 3,$ so I can plug them into the formulas. |
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For $r,$ I get $r = \sqrt{0^2 + 3^2} = \sqrt{9} = 3.$ |
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For $\theta,$ I get $\theta = \tan^{-1}(3/0).$ |
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This is undefined, since the tangent function is not defined at $0.$ |
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However, I can use the fact that the point $(0,3)$ lies on the positive $y$-axis, which has an angle of $\pi/2$ radians or $90^\circ.$ |
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Therefore, I can choose any angle in the range $(0,\pi/2)$ as the value of $\theta.$ |
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I will choose $\theta = \pi/2,$ since it is the simplest and most natural choice. |
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Therefore, the polar coordinates of the point $(0,3)$ are $(3,\pi/2).$ |
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# Answer |
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(3,\pi/2) |
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``` |